clear

cd "replication files/US data/"

use "US_data_clean_w1_respondent.DTA", clear

**sample description
sum college
sum female
tab age
sum dem_leaners
sum repub_leaners

**attrition by treatment 
reg end2 tips, robust 
est store attrit
**Table C4: Effect of media literacy intervention on attrition in U.S. study**
estout attrit using attrit.tex, replace varwidth(25) collabels("") cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**attrition on false news accuracy w1
reg end2 accuracy_fake_mean, robust  // yes
est store attrit_fake 

*media trust, media feelings, and average fake news belief in wave 1
*-political characteristics (partisanship and political knowledge)
*-demographic characteristics (race, sex, and age)
reg end2 massmedia_trust, robust
est store att_mass
reg end2 FT_media, robust
est store att_media
reg end2 repub_leaners dem_leaners, robust 
est store att_party
reg end2 polknow, robust  // yes
est store att_pk
reg end2 nonwhite, robust
est store att_nw
reg end2 female, robust 
est store att_fem
reg end2 i.agecat, robust // yes
est store att_age
**Table C5: Effect of demographics on attrition in U.S. study**
estout att_mass att_media att_party att_pk att_nw att_fem att_age attrit_fake using attrit_x.tex, replace varwidth(25) collabels("") cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**balance table
**dem_leaners repub_leaners polknow polint college female nonwhite ib1.agecat
**Table C3: Balance statistics for U.S. media literacy intervention**
balancetable tips female nonwhite agecat1 agecat2 agecat3 agecat4  college  polint polknow repub_leaners dem_leaners using "tipsbalance-US.tex", vce(robust) starlevels(+ 0.05 ++ 0.01 +++ 0.005) observationscolumn	 ctitles("Control" "Media Literacy Intervention" "Difference" "N") nonumbers noobservations replace

use "pulse_post_clean.DTA", clear 
**tips effects on pulse
reg totalfakebinary18 tips if nopulse==0, robust
est store A
reg totalfakecount18 tips if nopulse==0, robust
est store B

reg totalfcbinary tips if nopulse==0, robust
est store C
reg totalfccount tips if nopulse==0, robust
est store D

reg totalmsbinary tips if nopulse==0, robust
est store E
reg totalmscount tips if nopulse==0, robust
est store F

**Table C14: Effects of U.S. media literacy intervention on subsequent news consumption (ITT)**
estout A B C D E F using "tips-behavior-null.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**tips effects on pulse
ivreg2 totalfakebinary18 (r_tips=tips) if nopulse==0, robust
est store A
ivreg2 totalfakecount18 (r_tips=tips) if nopulse==0, robust
est store B

ivreg2 totalfcbinary (r_tips=tips) if nopulse==0, robust
est store C
ivreg2 totalfccount (r_tips=tips) if nopulse==0, robust
est store D

ivreg2 totalmsbinary (r_tips=tips) if nopulse==0, robust
est store E
ivreg2 totalmscount (r_tips=tips) if nopulse==0, robust
est store F

**Table C15: Effects of U.S. media literacy intervention on subsequent news consumption (ATT)**
estout A B C D E F using "tips-behavior-null-att.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.4f)) se(par fmt(%9.4f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

use "US_data_clean_w1_respondent.DTA", clear

**tips effects on pulse with pre-treatment vars

reg totalfakebinary18 tips totalfakebinary18_presurvey fakebinary_tips if nopulse==0, robust
est store pre_mod_fakebinary
reg totalfakecount18 tips totalfakecount18_presurvey fakecount_tips if nopulse==0, robust
est store pre_mod_fakecount

**Table A5: Effects of U.S. media literacy intervention on subsequent false news consumption by pre-treatment consumption**
esttab   pre_mod_fakebinary pre_mod_fakecount using pulse_pretreatment.tex, replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**pre-reshape real-fake mean acc models
reg mean_acc_diff tips, robust 
estimates store t4c5, title(Tips effect on fake-real w1)

ivreg2 mean_acc_diff (r_tips=tips), robust cluster(caseid)
est store att_diff
ivreg2 mean_acc_diff (alt_r_tips=tips), robust cluster(caseid)
est store calt

reg mean_acc_diff tips polknow polint FT_trump  affect_merged_leanersw1 conspiracy_mean, robust 
estimates store tA4c5, title(Tips effect on fake-real w1)

use "US_data_clean_w1_headline.DTA", clear 

/*tips main effect*/
reg accuracy tips i.dv if fake==1, robust cluster(caseid)
estimates store t4c1, title(Tips effect on fake news accuracy w1)
reg accuracy tips i.dv if real==1, robust cluster(caseid)
estimates store t4c3, title(Tips effect on real news accuracy w1)
reg accuracy tips i.dv if hyper==1, robust cluster(caseid) 
estimates store tips_hyperw1, title(Tips effect on hyper accuracy w1)

/*tips main effect-binary dv*/
reg binary_accuracy tips i.dv if fake==1, robust cluster(caseid)
est store a1
reg binary_accuracy tips i.dv if real==1, robust cluster(caseid)
est store b1
**Table C6: Effect of U.S. media literacy intervention on perceived accuracy by news type (binary)** [ITT] 
estout a1 b1 using "binary_itt.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

*ATT effects - Table 1
ivreg2 accuracy (r_tips=tips) i.dv if fake==1, robust cluster(caseid)
est store att_fake
ivreg2 accuracy (r_tips=tips) i.dv if real==1, robust cluster(caseid)
est store att_real
estout att_fake att_real att_diff using "Table1_att.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**ATT headline, w1, binary
ivreg2 binary_accuracy (r_tips=tips) i.dv if fake==1, robust cluster(caseid)
est store binary_att_fake
ivreg2 binary_accuracy (r_tips=tips) i.dv if real==1, robust cluster(caseid)
est store binary_att_real
**Table C6: Effect of U.S. media literacy intervention on perceived accuracy by news type (binary)** [ATT] 
estout binary_att_fake binary_att_real using "binary_att.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

*fake hyper
reg accuracy tips if dv==3, robust /*fake*/
est store bigtab1
reg accuracy tips if dv==6, robust  /*hyper*/
est store bigtab2
reg accuracy tips if dv==2, robust/*hyper*/
est store bigtab3
reg accuracy tips if dv==1, robust/*hyper*/
est store bigtab4
reg accuracy tips if dv==8, robust  /*fake*/
est store bigtab5
reg accuracy tips if dv==7, robust   /*fake*/
est store bigtab6
reg accuracy tips if dv==4, robust  /*fake*/
est store bigtab7
reg accuracy tips if dv==5, robust /*hyper*/
est store bigtab8

**Table C11: Effect of U.S. media literacy intervention on perceived accuracy (question-level)**
estout bigtab1 bigtab2 bigtab3 bigtab4 bigtab5 bigtab6 bigtab7 bigtab8 using "bigtab1.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

*real
reg accuracy tips if dv==14, robust 
est store bigtab9
reg accuracy tips if dv==9, robust
est store bigtab10
reg accuracy tips if dv==13, robust
est store bigtab11
reg accuracy tips if dv==12, robust 
est store bigtab12
reg accuracy tips if dv==10, robust  
est store bigtab13
reg accuracy tips if dv==15, robust  
est store bigtab14
reg accuracy tips if dv==11, robust 
est store bigtab15
reg accuracy tips if dv==16, robust 
est store bigtab16

**Table C11: Effect of U.S. media literacy intervention on perceived accuracy (question-level)**
estout bigtab9 bigtab10 bigtab11 bigtab12 bigtab13 bigtab14 bigtab15 bigtab16 using "bigtab2.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

*congenial question-level

*fake hyper
reg accuracy tips##dem_leaners##repub_leaners  if dv==3, robust /*fake*/
est store bigtab1c
reg accuracy tips##dem_leaners##repub_leaners  if dv==6, robust  /*hyper*/
est store bigtab2c
reg accuracy tips##dem_leaners##repub_leaners  if dv==2, robust/*hyper*/
est store bigtab3c
reg accuracy tips##dem_leaners##repub_leaners   if dv==1, robust/*hyper*/
est store bigtab4c
reg accuracy tips##dem_leaners##repub_leaners   if dv==8, robust  /*fake*/
est store bigtab5c
reg accuracy tips##dem_leaners##repub_leaners   if dv==7, robust   /*fake*/
est store bigtab6c
reg accuracy tips##dem_leaners##repub_leaners   if dv==4, robust  /*fake*/
est store bigtab7c
reg accuracy tips##dem_leaners##repub_leaners   if dv==5, robust /*hyper*/
est store bigtab8c

**Table A6: Effect of U.S. media literacy intervention on perceived accuracy by partisan congeniality (question-level)**
estout bigtab1c bigtab2c bigtab3c bigtab4c bigtab5c bigtab6c bigtab7c bigtab8c using "bigtab1c.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

*real
reg accuracy tips##dem_leaners##repub_leaners   if dv==14, robust 
est store bigtab9c
reg accuracy tips##dem_leaners##repub_leaners   if dv==9, robust
est store bigtab10c
reg accuracy tips##dem_leaners##repub_leaners   if dv==13, robust
est store bigtab11c
reg accuracy tips##dem_leaners##repub_leaners   if dv==12, robust 
est store bigtab12c
reg accuracy tips##dem_leaners##repub_leaners   if dv==10, robust  
est store bigtab13c
reg accuracy tips##dem_leaners##repub_leaners   if dv==15, robust  
est store bigtab14c
reg accuracy tips##dem_leaners##repub_leaners   if dv==11, robust 
est store bigtab15c
reg accuracy tips##dem_leaners##repub_leaners   if dv==16, robust 
est store bigtab16c

**Table A6: Effect of U.S. media literacy intervention on perceived accuracy by partisan congeniality (question-level)**
estout bigtab9c bigtab10c bigtab11c bigtab12c bigtab13c bigtab14c bigtab15c bigtab16c using "bigtab2c.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**US respondent level w1 alt
reg accuracy tips i.dv if fake==1, robust cluster(caseid)
ivreg2 accuracy (alt_r_tips=tips) i.dv if fake==1, robust cluster(caseid)
est store aalt
reg accuracy tips i.dv if real==1, robust cluster(caseid)
ivreg2 accuracy (alt_r_tips=tips) i.dv if real==1, robust cluster(caseid)
est store balt

**Table C7: Effect of U.S. media literacy intervention: Alternate treatment receipt definition**
estout aalt balt calt using "alt.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**Table 1: Effect of U.S. media literacy intervention on perceived accuracy by news type [ITT]
estout t4c1 t4c3 t4c5 using "main_effects_itt.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**oprobit
oprobit accuracy tips i.dv if fake==1, robust cluster(caseid)
est store oprob_fake
oprobit accuracy tips i.dv if real==1, robust cluster(caseid)
est store oprob_real

**Table C8: Effect of U.S. media literacy intervention on perceived accuracy by news type (ordered probit)**
estout oprob_fake oprob_real using "oprob.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

**tips main effects with LASSO covariates**
reg accuracy tips polknow polint FT_trump  affect_merged_leanersw1 conspiracy_mean i.dv if fake==1, robust cluster(caseid)
estimates store tA4c1, title(Tips effect on fake news accuracy w1)
reg accuracy tips polknow polint FT_trump  affect_merged_leanersw1 conspiracy_mean i.dv if real==1, robust cluster(caseid)
estimates store tA4c3, title(Tips effect on real news accuracy w1)
reg accuracy tips polknow polint FT_trump  affect_merged_leanersw1 conspiracy_mean i.dv if hyper==1, robust cluster(caseid)

**Table A7: Effect of U.S. media literacy intervention on perceived accuracy by news type with covariate adjustment**
estout tA4c1 tA4c3 tA4c5 using "LASSO.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**congenial
reg mean_acc_diff congenial##tips i.dv, robust cluster(caseid)
estimates store congenial_diff, title(congenial diff) 
reg accuracy congenial##tips i.dv, robust cluster(caseid)
reg accuracy congenial##tips i.dv  if fake==1, robust cluster(caseid) 
estimates store congenial_fake, title(congenial fake) 
reg accuracy congenial##tips i.dv  if hyper==1, robust cluster(caseid)
reg accuracy congenial##tips i.dv  if real==1, robust cluster(caseid)
estimates store congenial_real, title(congenial real) 

**Figure C1a: Effect of U.S. media literacy intervention on perceived accuracy of false news headline accuracy by partisan congeniality (w1)**

gen type=.
replace type=1 if fake==1
replace type=2 if hyper==1
replace type=3 if real==1

label def conglab 0 "Uncongenial" 1 "Congenial"
label val congenial conglab

label def tipslab 0 "Control" 1 "Media literacy intervention"
label val tips tipslab

cibar accuracy if fake==1 & (dem_leaners==1 | repub_leaners==1), over1(tips) over2(congenial) bargap(8) gap(35) graphopts(graphregion(fcolor(white) ifcolor(none)) plotregion(fcolor(none) lcolor(white) ifcolor(none) ilcolor(none)) ytitle("") scheme(lean1) yscale(r(1 4.1)) ylab(1 "Not at all accurate" 2 "Not very accurate" 3 "Somewhat accurate" 4 "Very accurate",angle(0) grid glcolor(gs3)) legend(row(1) pos(6) region(lpattern(solid) lcolor(black))))
graph export "accuracy-fake-tips-congenial-w1.pdf",replace

**congenial diff
reg mean_acc_diff congenial##tips, robust cluster(caseid)
estimates store congenial_diff, title(congenial diff) 

**moderation tests**

**crt
reg mean_acc_diff tips crt_average crt_tips i.dv, robust cluster(caseid)
reg accuracy tips crt_average crt_tips i.dv, robust cluster(caseid)
reg accuracy tips crt_average crt_tips i.dv  if fake==1, robust cluster(caseid)
estimates store crt_fake, title(crt fake) 
reg accuracy tips crt_average crt_tips i.dv  if hyper==1, robust cluster(caseid)
estimates store crt_hyper, title(crt hyper)
reg accuracy tips crt_average crt_tips i.dv  if real==1, robust cluster(caseid)
estimates store crt_real, title(crt real) 

*media trust 
reg mean_acc_diff tips massmedia_trust mediatrust_tips i.dv, robust cluster(caseid)
reg accuracy tips massmedia_trust mediatrust_tips i.dv, robust cluster(caseid)
reg accuracy tips massmedia_trust mediatrust_tips i.dv  if fake==1, robust cluster(caseid)
est store mmtrust_fake
reg accuracy tips massmedia_trust mediatrust_tips i.dv  if hyper==1, robust cluster(caseid)
est store mmtrust_hyper
reg accuracy tips massmedia_trust mediatrust_tips i.dv  if real==1, robust cluster(caseid)
est store mmtrust_real

*media FT
reg mean_acc_diff tips FT_media mediaFT_tips i.dv, robust cluster(caseid)
reg accuracy tips FT_media mediaFT_tips i.dv, robust cluster(caseid)
reg accuracy tips FT_media mediaFT_tips i.dv  if fake==1, robust cluster(caseid)
est store mediaFT_fake
reg accuracy tips FT_media mediaFT_tips i.dv  if hyper==1, robust cluster(caseid)
est store mediaFT_hyper
reg accuracy tips FT_media mediaFT_tips i.dv  if real==1, robust cluster(caseid)
est store mediaFT_real

*feelings toward Trump, 
reg mean_acc_diff tips FT_trump trump_tips i.dv, robust cluster(caseid)
reg accuracy tips FT_trump trump_tips i.dv, robust cluster(caseid)
reg accuracy tips FT_trump trump_tips i.dv  if fake==1, robust cluster(caseid)
est store trump_fake
reg accuracy tips FT_trump trump_tips i.dv  if hyper==1, robust cluster(caseid)
est store trump_hyper
reg accuracy tips FT_trump trump_tips i.dv  if real==1, robust cluster(caseid)
est store trump_real

*conspiracy predispositions, 
reg mean_acc_diff tips conspiracy_mean consp_tips i.dv, robust cluster(caseid)
reg accuracy tips conspiracy_mean consp_tips i.dv, robust cluster(caseid)
reg accuracy tips conspiracy_mean consp_tips i.dv  if fake==1, robust cluster(caseid)
est store consp_fake
reg accuracy tips conspiracy_mean consp_tips i.dv  if hyper==1, robust cluster(caseid)
est store consp_hyper
reg accuracy tips conspiracy_mean consp_tips i.dv  if real==1, robust cluster(caseid)
est store consp_real

*political interest 
reg mean_acc_diff tips polint polint_tips i.dv, robust cluster(caseid)
reg accuracy tips polint polint_tips i.dv, robust cluster(caseid)
reg accuracy tips polint polint_tips i.dv  if fake==1, robust cluster(caseid)
est store int_fake
reg accuracy tips polint polint_tips i.dv  if hyper==1, robust cluster(caseid)
est store int_hyper
reg accuracy tips polint polint_tips i.dv  if real==1, robust cluster(caseid)
est store int_real

*knowledge
reg mean_acc_diff tips polknow polknow_tips i.dv, robust cluster(caseid)
reg accuracy tips polknow polknow_tips i.dv, robust cluster(caseid)
reg accuracy tips polknow polknow_tips i.dv if fake==1, robust cluster(caseid)
est store know_fake
reg accuracy tips polknow polknow_tips i.dv if hyper==1, robust cluster(caseid)
est store know_hyper
reg accuracy tips polknow polknow_tips i.dv if real==1, robust cluster(caseid)
est store know_real

**count
reg accuracy tips totalfccount_presurvey fccount_tips i.dv  if fake==1, robust cluster(caseid)
est store FCcount_fake
reg accuracy tips totalfccount_presurvey fccount_tips i.dv  if real==1, robust cluster(caseid)
est store FCcount_real
reg accuracy tips totalfccount_presurvey fccount_tips i.dv  if hyper==1, robust cluster(caseid)
est store FCcount_hyper

**binary
reg accuracy tips totalfcbinary_presurvey fcbinary_tips i.dv  if fake==1, robust cluster(caseid)
est store FCbinary_fake
reg accuracy tips totalfcbinary_presurvey fcbinary_tips i.dv  if real==1, robust cluster(caseid)
est store FCbinary_real
reg accuracy tips totalfcbinary_presurvey fcbinary_tips i.dv  if hyper==1, robust cluster(caseid)
est store FCbinary_hyper

**pre-treatment fake news 
**count
reg accuracy tips totalfakecount18_presurvey fakecount_tips i.dv  if fake==1, robust cluster(caseid)
est store fakecount_fake
reg accuracy tips totalfakecount18_presurvey fakecount_tips i.dv  if real==1, robust cluster(caseid)
est store fakecount_real
reg accuracy tips totalfakecount18_presurvey fakecount_tips i.dv  if hyper==1, robust cluster(caseid)
est store fakecount_hyper

**binary
reg accuracy tips totalfakebinary18_presurvey fakebinary_tips i.dv  if fake==1, robust cluster(caseid)
est store fakebinary_fake
reg accuracy tips totalfakebinary18_presurvey fakebinary_tips i.dv  if real==1, robust cluster(caseid)
est store fakebinary_real
reg accuracy tips totalfakebinary18_presurvey fakebinary_tips i.dv  if hyper==1, robust cluster(caseid)
est store fakebinary_hyper

**Table A1: Exploratory moderation tests for U.S. media literacy intervention (A)
**Table A2: Exploratory moderation tests for U.S. media literacy intervention (B)
**Table A3: Exploratory moderation tests for U.S. media literacy intervention (C)
estout  crt_fake crt_real trump_fake trump_real consp_fake consp_real int_fake int_real know_fake know_real using "null_mods1.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)
estout mmtrust_fake mmtrust_real mediaFT_fake mediaFT_real using "null_mods2.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)
estout FCcount_fake FCcount_real FCbinary_fake FCbinary_real fakecount_fake fakecount_real fakebinary_fake fakebinary_real using "null_mods3.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**Table A4: Exploratory moderation tests for U.S. media literacy intervention (hyperpartisan headlines)
estout  crt_hyper trump_hyper consp_hyper int_hyper know_hyper mmtrust_hyper mediaFT_hyper  FCcount_hyper  FCbinary_hyper  fakecount_hyper fakebinary_hyper using "null_mods_hyper.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**source prominence effect
use "US_data_clean_w1_source_prominence.DTA", clear 

reg accuracy tips##lowfam if real==1, robust cluster(caseid)
est store lowfam

**Table C12: Effects of U.S. media literacy intervention on perceived accuracy of mainstream news by source prominence**
estout lowfam using "lowfam.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

*table G1
tab tips binary_accuracy if fake==1, row
tab tips binary_accuracy if hyper==1, row 
tab tips binary_accuracy if real==1 & lowfam==0, row
tab tips binary_accuracy if real==1 & lowfam==1, row

tab tips binary_accuracy if real==1, row 

**cohens d
bysort tips: su accuracy if fake==1

drop if fake != 1
esize twosample accuracy, by(tips) all

use "US_data_clean_w1_effects_by_headline.DTA", clear 

**Figure C2: Effect of media literacy intervention by baseline headline accuracy (U.S. sample)**
twoway (scatter effect accuracy0 if fake==1,scheme(lean1) xtitle("Perceived baseline accuracy") ytitle("Treatment effect (media literacy)",height(-8))) (scatter effect accuracy0 if hyper==1,legend(row(1) pos(6) region(lpattern(solid) lcolor(black)) lab(1 "False") lab(2 "Hyperpartisan") lab(3 "Mainstream"))) (scatter effect accuracy0 if real==1)
graph export "effectX3.pdf", replace

twoway (scatter effect accuracy0 if fake==1,scheme(lean1) xtitle("Perceived baseline accuracy") ytitle("Treatment effect (media literacy)",height(-8))) (scatter effect accuracy0 if hyper==1,legend(row(2) pos(6) region(lpattern(solid) lcolor(black)) lab(1 "False news") lab(2 "Hyperpartisan news") lab(3 "Mainstream news/low prominence") lab(4 "Mainstream news/high prominence"))) (scatter effect accuracy0 if low_p==1) (scatter effect accuracy0 if high_p==1)
graph export "effectX4.pdf", replace

use "US_data_clean_w2_headline.DTA", clear

*replicate binary accuracy for w2
tab tips binary_accuracy if fake==1, row
tab tips binary_accuracy if real==1, row

**prior exposure effect
**saw x tips
reg accuracyw2 saw##tips i.dv, robust cluster(caseid)
reg accuracyw2 saw##tips i.dv  if fake==1, robust cluster(caseid)
estimates store tips_prior_fake, title(tips prior exposure effect on fake news) 
reg accuracyw2 saw##tips i.dv  if hyper==1, robust cluster(caseid)
reg accuracyw2 saw##tips i.dv  if real==1, robust cluster(caseid)
estimates store tips_prior_real, title(tips prior exposure effect on real news) 

**Table C10: Effects of U.S. media literacy intervention on perceived accuracy by news type and prior exposure**
estout tips_prior_fake tips_prior_real using "tipsprior.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**tips effects at w2 (without saw)
reg accuracyw2 tips i.dv, robust cluster(caseid)
reg accuracyw2 tips i.dv  if fake==1, robust cluster(caseid)
estimates store t4c2, title(Tips effect on fake news w2) 
reg accuracyw2 tips i.dv  if hyper==1, robust cluster(caseid)
estimates store tips_hyperw2, title(Tips effect on hyper accuracy w2)
reg accuracyw2 tips i.dv  if real==1, robust cluster(caseid)
estimates store t4c4, title(Tips effect on real news w2) 

**Table C2: Effects of U.S. media literacy intervention on perceived accuracy of hyperpartisan news**
estout tips_hyperw1 tips_hyperw2 using "tipshyper.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**tips x congenial at w2
reg accuracyw2 congenial##tips i.dv  if fake==1, robust cluster(caseid)
estimates store congenial_fakew2, title(Tipsxcong effect on fake w2) 
reg accuracyw2 congenial##tips i.dv  if real==1, robust cluster(caseid)
estimates store congenial_realw2, title(Tipsxcong effect on real w2) 

**Table C9: Effects of U.S. media literacy intervention on perceived accuracy by news type and congeniality**
estout congenial_fake congenial_fakew2 congenial_real congenial_realw2 using "congenial.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**Figure C1b: Effect of U.S. media literacy intervention on perceived accuracy of false news headline accuracy by partisan congeniality (w2)
gen type2=.
replace type2=1 if fake==1
replace type2=2 if hyper==1
replace type2=3 if real==1

label def tipslab 0 "Control" 1 "Media literacy intervention"
label val tips tipslab

cibar accuracyw2 if fake==1 & (dem_leaners==1 | repub_leaners==1), over1(tips) over2(congenial) bargap(8) gap(35) graphopts(graphregion(fcolor(white) ifcolor(none)) plotregion(fcolor(none) lcolor(white) ifcolor(none) ilcolor(none)) ytitle("") scheme(lean1) yscale(r(1 4.1)) ylab(1 "Not at all accurate" 2 "Not very accurate" 3 "Somewhat accurate" 4 "Very accurate",angle(0) grid glcolor(gs3)) legend(row(1) pos(6) region(lpattern(solid) lcolor(black))))
graph export "accuracy-fake-tips-congenial-w2.pdf",replace

**table C1

**att w2 
ivreg2 accuracyw2 (r_tips=tips) dv if fake==1, robust cluster(caseid)
est store att_fakew2
ivreg2 accuracyw2 (r_tips=tips) dv if real==1, robust cluster(caseid)
est store att_realw2

use "US_data_clean_w2_respondent.DTA", clear

**itt diff
reg mean_acc_diffw2 tips, robust cluster(caseid)
est store itt_diffw2
**att diff
ivreg2 mean_acc_diffw2 (r_tips=tips), robust cluster(caseid)
est store att_diffw2

estout t4c1 t4c2 t4c3 t4c4 t4c5 itt_diffw2 using "c1_itt.tex",  replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)
estout att_fake att_fakew2 att_real att_realw2 att_diff att_diffw2 using "c1_att.tex",  replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex)

**tips effect on sharing
use "US_data_clean_w1_share.DTA", clear
**tips  effect on sharing w1 
reg sharex tips i.dv if fake==1, robust cluster(caseid)
estimates store sharefakew1x, title(Tips effect on sharing fake news w1) 
reg sharex tips i.dv if real==1, robust cluster(caseid)
estimates store sharerealw1x, title(Tips effect on sharing real news w1) 
reg sharex tips i.dv if hyper==1, robust cluster(caseid)
estimates store sharehyperw1x, title(Tips effect on sharing hyper-partisan news w1) 

use "US_data_clean_w2_share.DTA", clear
**tips  effect on sharing w2 
reg sharexw2 tips i.dv if fake==1, robust cluster(caseid)
estimates store sharefakew2x, title(Tips effect on sharing fake news w2) 
reg sharexw2 tips i.dv if real==1, robust cluster(caseid)
estimates store sharerealw2x, title(Tips effect on sharing real news w2) 
reg sharexw2 tips i.dv if hyper==1, robust cluster(caseid)
estimates store sharehyperw2x, title(Tips effect on sharing hyper-partisan news w2) 
**Table C13: Effects of U.S. media literacy intervention on sharing intention by news type**
estout sharefakew1x sharefakew2x sharerealw1x sharerealw2x sharehyperw1x sharehyperw2x using "c13_share.tex", replace varwidth(25) collabels("") cells(b(star fmt(%9.3f)) se(par fmt(%9.3f))) stats(r2 N, fmt(%9.2f %9.0f) labels("R^2" "N")) starlevels(* 0.05 ** 0.01 *** 0.005) style(tex) 

